Square Enix

Data Scientist – Recommendation

Square Enix

full-time

Posted on:

Location Type: Hybrid

Location: London • United Kingdom

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Tech Stack

AWSAzureCloudDockerKubernetesPythonPyTorchScikit-LearnSQLTensorflow

About the role

  • Design and implement recommendation engines using collaborative filtering, content-based methods, and rule-based approaches, tailored to both new releases and catalogue titles.
  • Integrate forecast outputs (e.g., awareness scores, purchase intent) into recommendation logic to personalize marketing actions.
  • Develop personalized marketing interventions (e.g., bundles, coupons, content surfacing) aligned with sales schedules and forecasted demand.
  • Conduct user behavior analysis to uncover actionable insights.
  • Path analysis to trace user journeys and identify drop-off points.
  • Predictive modeling to quantify drivers of engagement and conversion.
  • Finding cross-sell opportunities across multiple channels and product categories.
  • Collaborate with the Forecast team to align recommendation strategies with predictive models and business priorities.
  • Manage and version control codebases (e.g., Git), organize experiments, and improve pipeline robustness.
  • Communicate findings and recommendations clearly to stakeholders across business and technical teams.

Requirements

  • Demonstrable current proficiency in applied mathematics relevant to machine learning and business analytics (e.g., A-level Mathematics with grade A or A+ or equivalent).
  • Proficiency in Python and SQL for data analysis and model development.
  • Strong foundation in statistics, probability, and linear algebra.
  • Experience with recommender system techniques such as collaborative filtering, content-based recommendation, and rule-based logic.
  • Familiarity with ML frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).
  • Exposure to ML operations, including: Code versioning (e.g., Git), Experiment tracking, and Model deployment and monitoring (e.g., CI/CD pipelines, Vertex AI Pipelines), containerization and deployment tools (e.g., Docker, Kubernetes), cloud computing platforms (e.g., Google Cloud, AWS, Azure).
  • Strong delivery mindset, with the ability to work under tight deadlines and consistently drive business impact.
  • Excellent communication and collaboration skills, with the ability to work across data science, engineering, and business teams.
  • Experience integrating predictive models (e.g., awareness, intent, forecasted sales) into recommendation logic.
  • Familiarity with probabilistic modeling libraries (e.g., PyMC, Stan) and causal inference frameworks (e.g., DoWhy, EconML).
  • Experience designing and evaluating personalized marketing interventions.
  • Experience working with marketing or e-commerce data.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
PythonSQLapplied mathematicsstatisticsprobabilitylinear algebracollaborative filteringcontent-based recommendationrule-based logicpredictive modeling
Soft Skills
delivery mindsetcommunication skillscollaboration skillsability to work under tight deadlinesdrive business impact